Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as 'rules of thumb' but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.
About the Author:

Rafael Martí is Professor of Statistics and Operations Research at the University of Valencia, Spain. He received a doctoral degree in Mathematics from the University of Valencia in 1994. He has done extensive research in metaheuristics for hard optimization problems. Dr Martí has about 200 publications, half of them in indexed journals (JCR), including EJOR, Informs JoC, IIE Transactions, JOGO, C&OR, and Discrete and Applied Maths. He is the co-author of several monographic books: Scatter Search (Kluwer 2003), The Linear Ordering Problem (Springer 2011), and Metaheuristics for Business Analytics (Springer 2018), and has secured an American patent. Prof. Martí is currently Area Editor in the Journal of Heuristics, Associate Editor in the Math. Prog. Computation, and the Int. Journal of Metaheuristics. He is Senior Research Associate of OptTek Systems (USA), and has given about 50 invited and plenary talks. Dr. Martí has been invited Professor at the University of Colorado (USA), University of Molde (Norway), University of Graz (Austria), and University of Bretagne-Sud (France). He coordinates the Spanish Network on Metaheuristics, currently funded as a SEIO working group.

Panos Pardalos is a Distinguished Professor and the Paul and Heidi Brown Preeminent Professor in the Departments of Industrial and Systems Engineering at the University of Florida, and a world renowned leader in Global Optimization, Mathematical Modeling, and Data Sciences. He is a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for "scientific contributions that stand the test of time." Dr. Pardalos is also a Foreign Member of the Lithuanian Academy of Sciences, the Royal Academy of Spain, and the National Academy of Sciences of Ukraine. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization. He has published over 500 papers, edited/authored over 200 books and organized over 80 conferences.

Mauricio G. C. Resende grew up in Rio de Janeiro (BR), West Lafayette (IN-US), and Amherst (MA-US). He did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro. He obtained an MS in operations research from Georgia Tech and a PhD in operations research from the U. of California, Berkeley. He is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows. Dr. Resende has published over 200 papers on optimization and holds 15 U.S. patents. He has edited four handbooks, including the Handbook of Heuristics, the Handbook of Applied Optimization, and the Handbook of Optimization in Telecommunications, and is coauthor of the book "Optimization by GRASP." He sits on the editorial boards of several optimization journals, including Networks, J. of Global Optimization, R.A.I.R.O., and International Transactions in Operational Research. Prior to joining Amazon.com in 2014 as a Principal Research Scientist in the transportation area, Dr. Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research in New Jersey. Since 2016, Dr. Resende is also Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle